Abstract: A recipient node of a multi-node data partitioning landscape can receive, directly from a requesting machine without being handled by a master node, a first data request related to a table. A target node of a plurality of processing nodes can be identified to handle the data request. The determining can include the recipient node applying partitioning information to determine a target data partition of the plurality of data partitions to which the data request should be directed and mapping information associating each data partition of the plurality of data partitions with an assigned node of the plurality of processing nodes. The recipient node can redirect the data request to the target node so that the target node can act on the target data partition in response to the data request.

Abstract: In one embodiment the present invention includes an OLAP execution model using relational operations. In one embodiment, the present invention includes, a method comprising receiving a first query in an online analytic processor (OLAP) executing on one or more computers, the OLAP generating and comprising a model specifying a graph defining a plurality of nodes and a plurality of tiers, each node corresponding to a different operation on data. A second query is generated by the OLAP. The second query includes a plurality of layered subqueries each corresponding to one of the nodes in the graph for specifying the different operations on data. The second query is received in a relational engine coupled to the datastore. The relational engine executes the second query, and in accordance therewith, retrieves data.

Abstract: A database query of point data among two or more axes of a database is received. The database stores point data in distinct integer vectors with a shared dictionary. Thereafter, the dictionary is scanned to determine boundaries for each axis specified by the query. In response, results characterizing data responsive to the query within the determined boundaries for each axis are returned. Related apparatus, systems, techniques and articles are also described.

Abstract: In one embodiment the present invention includes an OLAP execution model using relational operations. In one embodiment, the present invention includes, a method comprising receiving a first query in an online analytic processor (OLAP) executing on one or more computers, the OLAP generating and comprising a model specifying a graph defining a plurality of nodes and a plurality of tiers, each node corresponding to a different operation on data. A second query is generated by the OLAP. The second query includes a plurality of layered subqueries each corresponding to one of the nodes in the graph for specifying the different operations on data. The second query is received in a relational engine coupled to the datastore. The relational engine executes the second query, and in accordance therewith, retrieves data.

Abstract: A spatial aggregation query of a database is processed by receiving data specifying a maximum bounded rectangle for point data responsive to the query and specifying one or more grid partitions of the maximum bounded rectangle (in which at least one of the partitions is partially aggregated. Thereafter, for each partition, a number of points responsive to the query in each partition and a center of gravity of the points in each partition is computed. Data characterizing the corresponding computed number of points and center of gravity is then provided (e.g., persisted, loaded, transmitted, displayed, etc.). Related apparatus, systems, techniques and articles are also described.

Abstract: A range k-nearest neighbor search query of a database is processed by first defining an inner rectangle bounded within a circle around a center point specified by the range k-nearest neighbor search query. Thereafter, a distance to the center point is calculated for each point within the inner rectangle. Query results are returned if k or more points are within the inner rectangle. Otherwise, at least one additional query is executed. Related apparatus, systems, techniques and articles are also described.

Abstract: A database query of point data among two or more axes of a database is received. The database stores point data in distinct integer vectors with a shared dictionary. Thereafter, the dictionary is scanned to determine boundaries for each axis specified by the query. In response, results characterizing data responsive to the query within the determined boundaries for each axis are returned. Related apparatus, systems, techniques and articles are also described.

Abstract: A query is received which causes an initial data flow graph that includes a plurality of nodes that are used to execute the query is generated. Thereafter, the initial data flow graph is optimized using a model optimizer that includes an optimizer framework and an application programming interface (API). The optimizer framework provides logic to restructure the initial data flow graph and a rules engine for executing one or more optimization rules. The API allows for registration of new optimization rules to be executed by the rules engine. Execution of the query is then initiated using the optimized data flow graph. Related apparatus, systems, techniques and articles are also described.

Abstract: A query is received, and in response, an initial data flow graph is generated that includes a plurality of nodes for executing the query with at least one of the nodes having at least one associated hint. The initial data flow graph is subsequently optimized using a model optimizer having a rules engine using a plurality rules to optimize the initial data flow graph. The at least one associated hint is used by the model optimizer to change how at least one of the plurality of rules is applied. Thereafter, execution of the query is initiated using the optimized data flow graph. Related apparatus, systems, techniques and articles are also described.

Abstract: A query statement is received that specifies a count distinct. Thereafter, a data flow graph that comprises a plurality of nodes for executing the query is generated. The nodes provide aggregation operations, sorting of results on join attributes and vertically appending columns of count distinct results with intermediate results from at least one of the aggregation operations. Thereafter, execution of the query is initiated using the data flow graph. Related apparatus, systems, techniques and articles are also described.

Abstract: A query is received and an initial data flow graph comprising a plurality of nodes is generated for executing the query. The initial data flow graph is optimized using a model optimizer that accesses at least one of a plurality of patterns to identify a matching pattern and executes at least one optimization rule associated with a matching pattern. Execution of the query is then initiated using the optimized data flow graph. Related apparatus, systems, techniques and articles are also described.

Abstract: A query statement is received that requires at least one calculated attribute. Thereafter, a data flow graph is generated that includes a plurality of nodes for executing the query. At least one of the nodes corresponds to the at least one calculated attribute and has at least one level of child nodes. The data flow graph is generated by generating at least one filter for each of the nodes corresponding to the at least one calculated attribute and by pushing down the generated filters to a corresponding child node. Once the data flow graph is generated, execution of the query can be initiated using the generated data flow graph. Related apparatus, systems, techniques and articles are also described.

Abstract: A dynamic split node defined within a calculation model can receive data being operated on by a calculation plan generated based on the calculation model. A partition specification can be applied to one or more reference columns in a table containing at least some of the received data. The applying can cause the table to be split such that a plurality of records in the table are partitioned according to the partition specification. A separate processing path can be set for each partition, and execution of the calculation plan can continue using the separate processing paths, each of which can be assigned to a processing node of a plurality of available processing nodes.

Abstract: A recipient node of a multi-node data partitioning landscape can receive, directly from a requesting machine without being handled by a master node, a first data request related to a table. A target node of a plurality of processing nodes can be identified to handle the data request. The determining can include the recipient node applying partitioning information to determine a target data partition of the plurality of data partitions to which the data request should be directed and mapping information associating each data partition of the plurality of data partitions with an assigned node of the plurality of processing nodes. The recipient node can redirect the data request to the target node so that the target node can act on the target data partition in response to the data request.

Abstract: Based on one or more first columns of a table partitioned at a first level over multiple first level partitions, a first partition criteria is evaluated for a data record. Each of the at least first level partitions is further partitioned into a same number of second-level partitions using a same second partitioning method for all first level partitions. One first level partition is identified for storage of the data record, for example by determining the one of the at least two data server processes using the first partition criteria and a first partitioning method that differs from the second partitioning method. Based on one or more second columns of the table, a second partition criteria is evaluated for the data record, and the data record is stored in one of the second-level partitions of the at least two first level partitions.

Abstract: A select query or a data referencing a calculation scenario is received by a database server from a remote application server. The specified calculation scenario is one of a plurality of calculation scenarios and it defines a data flow model that includes one or more calculation nodes. Each calculation node defines one or more operations to execute on the database server. Thereafter, the database server instantiates the specified calculation scenario and executes the operations defined by the calculation nodes of the instantiated calculation scenario to result in a responsive data set. This data set is then provided by the database server to the application server. Related apparatus, systems, techniques and articles are also described.

Abstract: A select query or a data referencing a calculation scenario is received by a database server from a remote application server. The specified calculation scenario is one of a plurality of calculation scenarios and it defines a data flow model that includes one or more calculation nodes. Each calculation node defines one or more operations to execute on the database server. Thereafter, the database server instantiates the specified calculation scenario and executes the operations defined by the calculation nodes of the instantiated calculation scenario to result in a responsive data set. This data set is then provided by the database server to the application server. Related apparatus, systems, techniques and articles are also described.

Abstract: In one embodiment the present invention includes an OLAP execution model using relational operations. In one embodiment, the present invention includes, a method comprising receiving a first query in an online analytic processor (OLAP) executing on one or more computers, the OLAP generating and comprising a model specifying a graph defining a plurality of nodes and a plurality of tiers, each node corresponding to a different operation on data. A second query is generated by the OLAP. The second query includes a plurality of layered subqueries each corresponding to one of the nodes in the graph for specifying the different operations on data. The second query is received in a relational engine coupled to the datastore. The relational engine executes the second query, and in accordance therewith, retrieves data.

Abstract: A select query or a data referencing a calculation scenario is received by a database server from a remote application server. The specified calculation scenario is one of a plurality of calculation scenarios and it defines a data flow model that includes one or more calculation nodes. Each calculation node defines one or more operations to execute on the database server. Thereafter, the database server instantiates the specified calculation scenario and executes the operations defined by the calculation nodes of the instantiated calculation scenario to result in a responsive data set. This data set is then provided by the database server to the application server. Related apparatus, systems, techniques and articles are also described.

Abstract: The present invention includes a database with a semantic layer for performing standard SQL and complex operations custom to one or more application. In one embodiment the present invention includes receiving a data request from an application in a database, the data request specifying standard SQL operations and custom application operations to be performed for said application. The data request is processed in a semantic layer of the database. The semantic layer processes the standard SQL operations and the custom application operations and generates a logical execution plan comprising the standard SQL operations and the custom application operations. The logical execution plan is converted into a physical execution plan, which is executed by the database to generate a data result. The data result may be sent back to the application.